Behavior Trees (BTs) is a robust framework for decision-making that is highly applicable in automated guided vehicles (AGVs), offering a way to manage complex tasks and respond to dynamic changes in their environment.To enhance the adaptability and efficiency of BTs in AGVs, we introduce a hybrid approach that integrates Genetic Programming (GP) and Reinforcement Learning (RL).The GP evolves the BT structure by selecting potential actions from an action pool, guided by tailored constraints that ensure the trees remain interpretable and relevant to AGV tasks.Meanwhile, to mitigate node dependency issues in BTs, we employ RL, which incorporates a parameterdependent dynamic updating (PDDU) algorithm to monitor and manage the relationships between parameters.Furthermore, we implement a weighted ϵ-greedy algorithm to refine the parameter update process.Our methodology is validated through simulated AGV scenarios, demonstrating that the evolved BT significantly improve AGV autonomy.This innovative fusion of GP and RL techniques sets a foundation for future developments in AGV technology, with potential applications extending beyond the factory floor to any environment where AGVs are deployed.

We investigate a rigorous simulation and testing-based validation method for autonomous driving systems that integrates an existing industrial simulator and a formally defined testing environment. The environment includes a scenario generator that drives the simulation process and a monitor that checks at runtime the observed behavior of the system against a set of system properties to be validated. The validation method consists in extracting from the simulator a semantic model of the simulated system including a metric graph, which is a mathematical model of the environment in which the vehicles of the system evolve. The monitor can verify properties formalized in a first-order linear temporal logic and provide diagnostics explaining their non-satisfaction. Instead of exploring the system behavior randomly as many simulators do, we propose a method to systematically generate sets of scenarios that cover potentially risky situations, especially for different types of junctions where specific traffic rules must be respected. We show that the systematic exploration of risky situations has uncovered many flaws in the real simulator that would have been very difficult to discover by a random exploration process.

Providing safety guarantees for Autonomous Vehicle (AV) systems with machine-learning based controllers remains a challenging issue. In this work, we propose Simplex-Drive, a framework that can achieve runtime safety assurance for machine-learning enabled controllers of AVs. The proposed Simplex-Drive consists of an unverified Deep Reinforcement Learning (DRL)-based advanced controller (AC) that achieves desirable performance in complex scenarios, a Velocity-Obstacle (VO) based baseline safe controller (BC) with provably safety guarantees, and a verified mode management unit that monitors the operation status and switches the control authority between AC and BC based on safety-related conditions. We provide a formal correctness proof of Simplex-Drive and conduct a lane-changing case study in dense traffic scenarios. The simulation experiment results demonstrate that Simplex-Drive can always ensure the operation safety without sacrificing control performance, even if the DRL policy may lead to deviations from the safe status.

As a newly proposed secure transport protocol, QUIC aims to improve the transport performance of HTTPS traffic and enable rapid deployment and evolution of transport mechanisms. QUIC is currently in the IETF standardization process and will potentially carry a significant portion of Internet traffic in the emerging future. An important safety goal of QUIC protocol is to provide effective data service for users. To aim this safety requirement, we propose a formal analysis method to analyze the safety of QUIC handshake protocol by using model checker SPIN and cryptographic protocol verifier ProVerif. Our analysis shows the counterexamples to safety properties, which reveal a design flaw in the current protocol specification. To this end, we also propose and verify a possible fix that is able to mitigate these flaws.

Time sensitive networking (TSN) is an emerging technology for in-vehicle networks, which has strict timing constraints and dependability requirements. The performance and efficiency of TSN depend on a reliable scheduling mechanism for real-time communications. Traditionally, the TSN scheduler is analyzed using simulations and testing, which are error-prone and inaccurate. In this article, we employ a timed model checker UPPAAL to formally model and analyze the TSN scheduler with a consideration of its transmission latency and time utilization. We first use the build-in assertion in UPPAAL to verify the deadlock-free property, safety property, and starvation-free property of our model. Then, we calculate the time utilization and the transmission delay of audio video bridging data frame under two scheduling mechanisms according to the simulation process. Then, we compared the time performance of this two scheduling mechanisms and found that each of them has their own advantages and disadvantages. This article provides a formal analysis framework for investigating scheduling strategies in TSN, which facilitates the designers to have a better understanding and development of TSN schedulers in the future.

We study a novel principle for safe and efficient collision avoidance that adopts a mathematically elegant and general framework making as much as possible abstraction of the controlled vehicle’s dynamics and of its environment. Vehicle dynamics is characterized by pre-computed functions for accelerating and braking to a given speed. Environment is modeled by a function of time giving the free distance ahead of the controlled vehicle under the assumption that the obstacles are either fixed or are moving in the same direction. The main result is a control policy enforcing the vehicle’s speed so as to avoid collision and efficiently use the free distance ahead, provided some initial safety condition holds.The studied principle is applied to the design of a synchronous controller. We show that the controller is safe by construction. Furthermore, we show that the efficiency strictly increases for decreasing granularity of discretization. We present the implementation and experimental evaluations in the Carla autonomous driving simulator and investigate various performance issues.

Software vulnerabilities are usually caused by design flaws or implementation errors, which could be exploited to cause damage to the security of the system. At present, the most commonly used method for detecting software vulnerabilities is static analysis. Most of the related technologies work based on rules or code similarity (source code level) and rely on manually defined vulnerability features. However, these rules and vulnerability features are difficult to be defined and designed accurately, which makes static analysis face many challenges in practical applications. To alleviate this problem, some researchers have proposed to use neural networks that have the ability of automatic feature extraction to improve the intelligence of detection. However, there are many types of neural networks, and different data preprocessing methods will have a significant impact on model performance. It is a great challenge for engineers and researchers to choose a proper neural network and data preprocessing method for a given problem. To solve this problem, we have conducted extensive experiments to test the performance of the two most typical neural networks (i.e., Bi-LSTM and RVFL) with the two most classical data preprocessing methods (i.e., the vector representation and the program symbolization methods) on software vulnerability detection problems and obtained a series of interesting research conclusions, which can provide valuable guidelines for researchers and engineers. Specifically, we found that 1) the training speed of RVFL is always faster than Bi-LSTM, but the prediction accuracy of Bi-LSTM model is higher than RVFL; 2) using doc2vec for vector representation can make the model have faster training speed and generalization ability than using word2vec; and 3) multi-level symbolization is helpful to improve the precision of neural network models.

BIP is a component-based framework for system design that has important industrial applications. BIP is built on three pillars: behavior, interaction, and priority. In this paper, we introduce first-order interaction logic (FOIL) that extends BIP to systems parameterized in the number of components. We show that FOIL captures classical parameterized architectures such as token-passing rings, cliques of identical components communicating with rendezvous or broadcast, and client-server systems. Although the BIP framework includes efficient verification tools for statically-defined systems, none are available for parameterized systems with an unbounded number of components. The parameterized model checking literature contains a wealth of techniques for systems of classical architectures. However, application of these results requires a deep understanding of parameterized model checking techniques and their underlying mathematical models. To overcome these difficulties, we introduce a framework that automatically identifies parameterized model checking techniques applicable to a BIP design. To our knowledge, it is the first framework that allows one to apply prominent parameterized model checking results in a systematic way.

This paper presents SeBip, the first symbolic executor for component-based systems modeled in BIP. To tackle the path explosion problem, SeBip combines partial order reduction technique to reduce the number of interactions to be explored during executing the system symbolically. An experimental evaluation has been carried out to demonstrate the scalability of SeBip on detecting bugs.

ABSTRACT This paper addresses radio frequency identification (RFID) authentication and ownership transfer in offline scenarios. Four typical related works are reviewed in detail. A series of shortcomings and vulnerabilities of them are pointed out. A new RFID authentication protocol based on a novel tag‐owner‐assisting architecture is proposed, making a tag's owner an essential participant of the RFID authentication process. The proposed protocol is distinguished from existing works in providing ownership transfer, access control, and mutual authentication without any centralized database neither on a backend server nor in a reader. The security of the proposed protocol is verified by using automated validation of Internet security protocols and applications tool. The proposed protocol is server‐less, simple, scalable, untraceable, and device‐independent. These features are simultaneously achieved in a single RFID authentication protocol for the first time. Copyright © 2014 John Wiley & Sons, Ltd.

The research on the anonymity of original McEliece PKC points out that the original McEliece PKC fails to hold the property of key privacy. A novel semantically secure variant of McEliece PKC is proposed, and proved its anonymity formally in standard model. As far as we know, this is the first attempt to investigate the property of key privacy in McEliece PKC in literature.